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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(1): 24-30 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.477

A Fuzzy GM(1,1) Model Based Possibility Check for Predicting CO2 Emissions

Zhiyi Meng
Abstract—Increasing emissions of CO2 and other greenhouse gases as a result of human activities have bring about global warming, which is one of the major threats now confronting the environment. CO2 have impact on the environment is the greatest as it accounts for the largest share of total GHG. The greenhouse effect will further destroy the environment for humans and all other living beings, threatening the existence of humankind if anthropogenic CO2 emissions are allowed to increase without limits. The reduction of CO2 emissions has become a key issue that must be addressed for the protection of the environment. This paper aims to develop a fuzzy gray prediction model on CO2 emissions, and the world natural and cultural heritage area will be taken as an example. The fuzzy possibility is used to check the error of the proposed model, and the test results show that the accuracy of the model is quite high, providing a scientific basis for policy makers.

Index Terms—CO2 emission, fuzzy gray prediction, world natural and cultural heritage area, fuzzy possibility.

Zhiyi Meng is with the Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, China (e-mail: zhiyimengscu@sina.com).

[PDF]

Cite: Zhiyi Meng, "A Fuzzy GM(1,1) Model Based Possibility Check for Predicting CO2 Emissions," International Journal of Machine Learning and Computing vol. 5, no. 1, pp. 24-30, 2015.

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